9 accepted papers
Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks. In this paper, we first propose $\textbf{ARLArena}$, a fair and systematic analysis framework that encompasses a broad spectrum of ARL algorit…
Modern large language model (LLM) serving increasingly depends on efficient long-context processing and generation mechanisms, including sparse attention, retrieval-augmented generation (RAG), and compressed contextual memory, to support complex reasoning. We show that these optimizations can be uni…
Large language models (LLMs) based on transformer architecture have shown outstanding performance across numerous real-world tasks. However, the autoregressive nature of these models makes the inference process slow and costly. Speculative decoding has emerged as a promising solution, leveraging a…
Transformer-based large language models (LLM) have been widely used in language processing applications. However, due to the memory constraints of the devices, most of them restrict the context window. Even though recurrent models in previous works can memorize past tokens to enable unlimited contex…
High-level synthesis (HLS) is a widely used tool in designing Field Programmable Gate Array (FPGA). HLS enables FPGA design with software programming languages by compiling the source code into an FPGA circuit. The source code includes a program (called ``kernel'') and several pragmas that instruct…
Large language models (LLMs) have achieved remarkable success across diverse tasks, yet their inference processes are hindered by substantial time and energy demands due to single-token generation at each decoding step. While previous methods such as speculative decoding mitigate these inefficiencie…
High-level synthesis (HLS) aims to raise the abstraction layer in hardware design, enabling the design of domain-specific accelerators (DSAs) like field-programmable gate arrays (FPGAs) using C/C++ instead of hardware description languages (HDLs). Compiler directives in the form of pragmas play a cr…
Customized accelerators provide gains of performance and efficiency in specific domains of applications. Sparse data structures and/or representations exist in a wide range of applications. However, it is challenging to design accelerators for sparse applications because no architecture or performan…
In video decoder applications, motion compensation (MC) is bandwidth consuming because of the non-regular memory access. Especially with the popularity of UHD video and the development of new coding standard (HEVC), external memory bandwidth becomes a crucial bottleneck. In this paper, we propose an…